Peek-a-Boo Reasoning: Contrastive Region Masking in MLLMs

📝 Original Info

• Title: Peek-a-Boo Reasoning: Contrastive Region Masking in MLLMs
• ArXiv ID: 2512.08976
• Date: 2025-12-03
• Authors: Isha Chaturvedi, Anjana Nair, Yushen Li, Adhitya Rajendra Kumar, Kevin Zhu, Sunishchal Dev, Ashwinee Panda, Vasu Sharma

📝 Abstract

We introduce Contrastive Region Masking (CRM), a training free diagnostic that reveals how multimodal large language models (MLLMs) depend on specific visual regions at each step of chain-of-thought (CoT) reasoning. Unlike prior approaches limited to final answers or attention maps, CRM provides causal, step-level attribution by systematically masking annotated regions and contrasting the resulting reasoning traces with unmasked baselines. Applied to datasets such as VisArgs[1], CRM reveals distinct failure modes: some models preserve reasoning structure, but hallucinate when evidence is missing, while others ground tightly to visual cues yet collapse under perturbations. By shifting the evaluation from correctness of answers to faithfulness of reasoning, CRM reframes visual benchmarks as diagnostic tools, highlighting the need for multimodal evaluation frameworks that measure not just performance, but also robustness and fidelity of reasoning.

📄 Full Content

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